Introduction: The map of diseases is usually constructed using the information from diseases incidnce in some regions. Some factors, such as measurment error and rapid variation of diseases rates in different regions make maps so wiggly that their interpretation becomes difficult. Therefore these maps must be smoothed using statisical methods. Methods: Since disease rates of different regions reflect an spatial correlation structure, in this paper the spatial correlation structure of data is specified by fitting a variogram model, then kriging as a best linear unbiased prediction method is used to make a smooth map of diseases. Results: The tuberculosis incidence rates of 262 counties of Iran are used to demonstrate the application and accuracy of the diseases mapping method presented in this paper. The smoothing map of tuberculosis disease, obtained by kriging method shows the geographical trend of the disease in Iran. In this map, central and western regions of Iran have minimum incidence rates, and it gradually increases toward the eastern boundaries. Discussion: The object of this article is introducing kriging method for disease mapping and tuberculosis disease is used to demonstrate the application of this method. There is on dubt that the numerical results of prediction and mapping can be affected by undercount in the smir positive (S +) tuberculosis data, which are gathered by the office for campaigning against diseases. However this method has a wide application in different areas of medical sciences. such as geographical epidemiology of diseases, environmental health and environmental engineering.